Our proposed Visual Clustering algorithm is illustrated in Figure REF  and consists of four main steps that we describe in this section. Consider the dataset \(X \in \mathbb {R}^{m \times 2}\)  that we would like to cluster. We start by the plotting step where \(X\)  is represented in a two-dimensional matrix form, denoted by \(I(X)\) . This is done by first linearly shifting the values of both features to be \(\in [0,256]\)  and then filling a zero-initialized \(256\times 256\)  matrix by a value of 1 for each sample in the dataset according to its coordinates. In case the dataset was high-dimensional, we apply Principal Component Analysis (PCA) and use the first two principle components as features. The matrix, which can be visualized as an image, is then fed as an input to the second step of binary segmentation where a pre-trained binary segmentation model is used. We adopt the U-Net architecture [1]} for binary segmentation and train it in supervised fashion on images of plotted datasets, which were synthetically generated, and their binary masks. The U-Net model predicts a pixel-level binary mask \(\hat{M}(I(X))\) .
